论文部分内容阅读
温度对象种类繁多 ,普遍具有时间常数大、纯滞后时间长、时变性较明显等特点 ,这使得基于被控对象数学模型的常规控制方法、模糊控制方法及专家智能控制等方法在温度控制中有较大的局限性而无法获得理想的控制效果。针对以上情况 ,本文采用具有自学习能力的单神经元自适应智能控制算法来对温度对象实施控制 ,该算法简单 ,易于实现。最后 ,研制出基于该算法和 16位单片机 (80C196KB)的新型智能温度控制仪 ,经仿真及实际应用证明 ,该控制仪具有较好的控制效果和应用前景
There are a great variety of temperature objects, which generally have the characteristics of large time constant, long time delay and obvious time variability. This makes the conventional control methods, fuzzy control methods and expert intelligent control methods based on the mathematic model of the controlled object have temperature control Larger limitations can not be the ideal control effect. In view of the above, this paper uses a self-learning single neuron adaptive intelligent control algorithm to control the temperature object, the algorithm is simple and easy to implement. Finally, a new intelligent temperature controller based on this algorithm and 16-bit microcomputer (80C196KB) is developed. The simulation and practical application prove that the controller has good control effect and application prospect